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In view of the lack of considering index weight and less nutritional status classification in maize crop’s nutritional status identification, an identification method of maize crop’s nutritional status based on index weight is studied. Based on the five aspects of Agronomic and soil properties, 15 identification indexes such as plant height and soil available phosphorus content are selected to construct the identification index system of maize crop’s nutritional status. Through the evidence fusion process, the subjective weight calculation method is combined with the objective weight calculation method to calculate each identification index system. The nutritional status of maize crops is divided into nine grades: extreme poor nutrition to extreme severe eutrophication. Samples are generated by random interpolation between the values of grade standard domain. The probabilistic neural network recognition model is constructed, and the randomly generated samples are used to train and test the model to obtain the recognition model architecture that meets the accuracy requirements. The weight of each index and the normalized sample index matrix are calculated and input into the trained recognition model to obtain the recognition results of nutritional status of corn crop samples. The test results show that the index weight obtained by this method has higher reliability and can meet the application needs of maize crop’s nutritional status identification. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background and purpose: Clinical symptoms and laboratory indices for acute inflammatory demyelinating polyneuropathy (AIDP), a variant of Guillain−Barré syndrome, and acute‐onset chronic inflammatory demyelinating polyneuropathy (A‐CIDP) were analyzed to identify factors that could contribute to early differential diagnosis. Methods: A retrospective chart review was performed on 44 AIDP and 44 A‐CIDP patients looking for any demographic characteristics, clinical manifestations or laboratory parameters that might differentiate AIDP from acutely presenting CIDP. Results: In Guillain−Barré syndrome patients (N = 63), 69.84% (N = 44) were classified as having AIDP, 19.05% (N = 12) were found to have acute motor axonal neuropathy, 6.35% (N = 4) were found to have acute motor and sensory axonal neuropathy, and 4.76% (N = 3) were found to have Miller Fisher syndrome. Serum uric acid (UA) was higher in A‐CIDP patients (329.55 ± 72.23 μmol/L) than in AIDP patients (221.08 ± 71.32 μmol/L) (p = 0.000). Receiver operating characteristic analyses indicated that the optimal UA cutoff was 283.50 μmol/L. Above this level, patients were more likely to present A‐CIDP than AIDP (specificity 81.80%, sensitivity 81.80%). During the follow‐up process, serum samples were effectively collected from 19 AIDP patients during the rehabilitation phase and 28 A‐CIDP patients during the remission stage, and it was found that UA levels were significantly increased in A‐CIDP (remission) (298.9 ± 90.39 μmol/L) compared with AIDP (rehabilitation) (220.1 ± 108.2 μmol/L, p = 0.009). Conclusion: These results suggest that serum UA level can help to differentiate AIDP from A‐CIDP with high specificity and sensitivity, which is helpful for early diagnosis and guidance of treatment. [ABSTRACT FROM AUTHOR]

Abstract Background Tuberculosis is a chronic infectious disease caused by mycobacterium tuberculosis (MTB) and is the ninth leading cause of death worldwide. It is still difficult to distinguish active TB from latent TB,but it is very important for individualized management and treatment to distinguish whether patients are active or latent tuberculosis infection. Methods A total of 220 subjects, including active TB patients (ATB, n = 97) and latent TB patients (LTB, n = 113), were recruited in this study .46 features about blood routine indicators and the VCS parameters (volume, conductivity, light scatter) of neutrophils(NE), monocytes(MO), and lymphocytes(LY) were collected and was constructed classification model by four machine learning algorithms(logistic regression(LR), random forest(RF), support vector machine(SVM) and k-nearest neighbor(KNN)). And the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC) to estimate of the model’s predictive performance for dentifying active and latent tuberculosis infection. Results After verification,among the four classifications, LR and RF had the best performance (AUROC = 1, AUPRC = 1), followed by SVM (AUROC = 0.967, AUPRC = 0.971), KNN (AUROC = 0.943, AUPRC = 0.959) in the training set. And LR had the best performance (AUROC = 0.977, AUPRC = 0.957), followed by SVM (AUROC = 0.962, AUPRC = 0.949), RF (AUROC = 0.903, AUPRC = 0.922),KNN(AUROC = 0.883, AUPRC = 0.901) in the testing set. Conclusions The machine learning algorithm classifier based on leukocyte VCS parameters is of great value in identifying active and latent tuberculosis infection.

Background: Tuberculosis is a chronic infectious disease caused by mycobacterium tuberculosis (MTB) and is the ninth leading cause of death worldwide. It is still difficult to distinguish active TB from latent TB,but it is very important for individualized management and treatment to distinguish whether patients are active or latent tuberculosis infection. Methods: A total of 220 subjects, including active TB patients (ATB, n = 97) and latent TB patients (LTB, n = 113), were recruited in this study.46 features about blood routine indicators and the VCS parameters (volume, conductivity, light scatter) of neutrophils(NE), monocytes(MO), and lymphocytes(LY) were collected and was constructed classification model by four machine learning algorithms(logistic regression(LR), random forest(RF), support vector machine(SVM) and k-nearest neighbor(KNN)). And the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC) to estimate of the model's predictive performance for dentifying active and latent tuberculosis infection. Results: After verification,among the four classifications, LR and RF had the best performance (AUROC = 1, AUPRC = 1), followed by SVM (AUROC = 0.967, AUPRC = 0.971), KNN (AUROC = 0.943, AUPRC = 0.959) in the training set. And LR had the best performance (AUROC = 0.977, AUPRC = 0.957), followed by SVM (AUROC = 0.962, AUPRC = 0.949), RF (AUROC = 0.903, AUPRC = 0.922),KNN(AUROC = 0.883, AUPRC = 0.901) in the testing set. Conclusions: The machine learning algorithm classifier based on leukocyte VCS parameters is of great value in identifying active and latent tuberculosis infection. [ABSTRACT FROM AUTHOR]

Objective: Our study aimed to assess the predictive value of the preoperative neutrophil-to-lymphocyte ratio(NLR) in distinguishing sarcomatoid renal cell carcinoma (SRCC) from clear cell renal cell carcinoma(CCRCC) and to developing a nomogram based on the preoperative NLR and other factors to distinguish SRCC from CCRCC. Materials and methods: The database involved 280 patients, including 46 SRCC and 234 CCRCC. logistic analysis was conducted to select the variables associated with identifying SRCC preoperatively, and subgroup analysis was used to further validate the ability of NLR with preoperative identification of SRCC.In addition, The data were randomly separated into a training cohort (n=195) and a validation cohort(n=85). And an NLR-based nomogram was plotted based on the logistic analysis results. The nomogram was evaluated according to its discrimination, consistency, and clinical benefits. Results: Multivariate analysis indicated that NLR, flank pain, tumor size, and total cholesterol(TC) were independent risk factors for identifying SRCC. The results of subgroup analysis showed that higher NLR was associated with a higher probability of SRCC in most subgroups. The area under the curve(AUC) of the training and validation cohorts were 0.801 and 0.738, respectively. The results of the calibration curve show high consistency between predicted and actual results. Decision Curve Analysis(DCA) showed clinical intervention based on the model was beneficial over most of the threshold risk range. Conclusion: NLR is a potential indicator for preoperative differentiation of SRCC and CCRCC, and the predictive model constructed based on NLR has a good predictive ability. The new model could provide suggestions for the early identification of SRCC. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Currently, it is difficult to extract the depth feature of the frontal emergency stops dangerous activity signal, which leads to a decline in the accuracy and efficiency of the frontal emergency stops the dangerous activity. Therefore, a recognition for frontal emergency stops dangerous activity algorithm based on Nano Internet of Things Sensor (NIoTS) and transfer learning is proposed. First, the NIoTS is installed in the athlete's leg muscles to collect activity signals. Second, the noise component in the activity signal is removed using the de-noising method based on mathematical morphology. Finally, the depth feature of the activity signal is extracted through the deep transfer learning model, and the Euclidean distance between the extracted feature and the depth feature of the frontal emergency stops dangerous activity signal is compared. If the European distance is small, it can be judged as the frontal emergency stops dangerous activity, and the frontal emergency stops dangerous activity recognition is realized. The results show that the average time delay of activity signal acquisition of the algorithm is low, the signal-to-noise ratio of the action signal is high, and the activity signal mean square error is low. The variance of the frontal emergency stops dangerous activity recognition does not exceed 0.5. The difference between the appearance time of the dangerous activity and the recognition time of the algorithm is 0.15 s, it can accurately and quickly recognize the frontal emergency stops the dangerous activity. [ABSTRACT FROM AUTHOR]

ObjectiveOur study aimed to assess the predictive value of the preoperative neutrophil-to-lymphocyte ratio(NLR) in distinguishing sarcomatoid renal cell carcinoma (SRCC) from clear cell renal cell carcinoma(CCRCC) and to developing a nomogram based on the preoperative NLR and other factors to distinguish SRCC from CCRCC.Materials and methodsThe database involved 280 patients, including 46 SRCC and 234 CCRCC. logistic analysis was conducted to select the variables associated with identifying SRCC preoperatively, and subgroup analysis was used to further validate the ability of NLR with preoperative identification of SRCC.In addition, The data were randomly separated into a training cohort(n=195) and a validation cohort(n=85). And an NLR-based nomogram was plotted based on the logistic analysis results. The nomogram was evaluated according to its discrimination, consistency, and clinical benefits.ResultsMultivariate analysis indicated that NLR, flank pain, tumor size, and total cholesterol(TC) were independent risk factors for identifying SRCC. The results of subgroup analysis showed that higher NLR was associated with a higher probability of SRCC in most subgroups. The area under the curve(AUC) of the training and validation cohorts were 0.801 and 0.738, respectively. The results of the calibration curve show high consistency between predicted and actual results. Decision Curve Analysis(DCA) showed clinical intervention based on the model was beneficial over most of the threshold risk range.ConclusionNLR is a potential indicator for preoperative differentiation of SRCC and CCRCC, and the predictive model constructed based on NLR has a good predictive ability. The new model could provide suggestions for the early identification of SRCC.

Since 2013, frequent haze pollution events in China have been attracting public attention, generating a demand to identify the haze areas using satellite observations. Many studies of haze recognition algorithms are based on observations from space-borne imagers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Advanced Himawari Imager (AHI). Since the haze pixels are frequently misidentified as clouds in the official cloud detection products, these algorithms mainly focus on recovering them from clouds. There are just a few studies that provide a more precise distinction between haze and clear pixels. The Medium Resolution Imaging Spectrometer II (MERSI-II), the imager aboard the FY-3D satellite, has similar bands to those of MODIS, hence, it appears to have equivalent application potential. This study proposes a novel MERSI haze mask (MHAM) algorithm to directly categorize haze pixels in addition to cloudy and clear ones. This algorithm is based on the fact that cloudy and clear pixels exhibit opposing visible channel reflectance and infrared channel brightness temperature characteristics, and clear pixels are relative brighter, and as well as this, there is a positive difference between their apparent reflectance values, at 0.865 μm and 1.64 μm, respectively, over bright surfaces. Compared with the Aqua/MODIS and MERSI-II official cloud detection products, these two datasets treat the dense aerosol loadings as certain clouds, possible clouds and possible clear pixels, and they treat distinguished light or moderate haze as possible clouds, possible clear pixels and certainly clear pixels, while the novel algorithm is capable of demonstrating the haze region's boundary in a manner that is more substantially consistent with the true color image. Using the PM2.5 (particle matter with a diameter that is less than 2.5 μm) data monitored by the national air quality monitoring stations as the test source, the results indicated that when the ground-based PM2.5 ≥ 35 μg/cm3 is considered to be haze days, the samples with the recognition rate that is higher than 85% accounted for 72.22% of the total samples. When PM2.5 ≥ 50 μg/cm3 is considered as haze days, 83.33% of the samples had an identification rate that was higher than 85%. A cross-comparison with similar research methods showed that the method proposed in this study had better sensitivity to bright surface clear and haze areas. This study will provide a haze mask for subsequent quantitative inversion of aerosol characteristics, and it will further exert the application benefits of MERSI-II instrument aboard on FY3D satellite. [ABSTRACT FROM AUTHOR]

Copyright of Al-Tadris: Jurnal Pendidikan Bahasa Arab is the property of IAIN Tulungagung, Arabic Education Department, Faculty of Education & Teachers Training and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ObjectiveThymoid carcinoma of the thyroid gland is a rare thyroid tumor, which is often presented in case reports.MethodsThe clinical data of two patients with thymic carcinoma of the thyroid gland were retrospectively reviewed.ResultsCase 1: a middle-aged woman who was admitted to the hospital because of “progressive enlargement of the anterior cervical mass for 8 months.” Color Doppler ultrasound and CT showed malignant tumor with high possibility of bilateral cervical lymph node metastasis. Total thyroidectomy and bilateral central cervical lymph node dissection were performed. Lymph node biopsy showed the metastasis of small cell undifferentiated thyroid carcinoma. Because the biopsy pathological result was not consistent with the pathology of the primary lesion, immunohistochemistry was performed again, and the final diagnosis was thymic carcinoma in the thyroid gland. Case 2: the patient was an elderly man who was admitted to the hospital due to hoarseness for half a month. During the operation, the tumor invaded the trachea, esophagus, internal jugular vein, common carotid artery, and surrounding tissues. Palliative resection of the tumor was performed. The tumor postoperative pathology suggested thymoid carcinoma of the thyroid gland. It recurred and compressed the trachea 4 months after the operation, resulting in dyspnea of the patient, and finally tracheotomy was performed to alleviate the symptoms.ConclusionCase 1 showed multiple differences in pathological diagnosis, suggesting that the lack of specific imaging and clinical manifestations of thymoid-differentiated thyroid carcinoma made the diagnosis so difficult. Case 2 progressed rapidly, suggesting that thymoid-differentiated thyroid carcinoma was not always inert, and the treatment and follow-up should follow the principle of individualization.

What Is Known and Objective: The technique of highlighted capital letters, Tall Man lettering, is a tool that allows differentiating the names of similar drugs in a way that contributes to reduce medication errors related to the drug identification. The objective was to implement and monitor the application protocol of the Tall Man lettering tool in drug information systems in the healthcare environment to improve the quality of care and patient safety in the pharmacy service and the medical institution. Methods: Scope: Tertiary general hospital with 1000 beds in which a Tall Man lettering application protocol was approved in the pharmacy service information systems. Design: Retrospective observational study. Period: 2019–2021. Population: Information systems and databases used in the pharmacy service. Working group: Made up of people in charge of information areas and systems. Implementation: Five phases were defined: organization of the working group, selection of drug names and information systems, establishment of actions, their execution, and results monitoring. Variables: Number of pharmaceutical specialties, names of active ingredient and brands incorporated in the information systems. Results and Discussion: The application of Tall Man lettering was authorized in 13/13 information systems, six have been fully implemented and seven are in the database update phase. Of the implanted systems, a total of 210 drug names have been modified (168/210 active ingredient and 42/210 brand names), corresponding to 659 pharmaceutical specialties. What Is New and Conclusion: The application of Tall Man lettering in hospital information systems is a tool for improving the quality of the pharmacy service and guarantees the safety of medicines in the hospital. [ABSTRACT FROM AUTHOR]

Carmesin has extended Robertson and Seymour's tree-of-tangles theorem to the infinite tangles of locally finite infinite graphs. We extend it further to the infinite tangles of all infinite graphs. Our result has a number of applications for the topology of infinite graphs, such as their end spaces and their compactifications. [ABSTRACT FROM AUTHOR]

The differential diagnosis of renal tumors and abscesses is crucial owing to their different treatments. Although antibacterial administration and radiological examination are excellent means for distinction, misdiagnosis is common and may lead to severe consequences, such as the need for nephrectomy. Here, we report a case involving a 52-year-old Asian woman with a renal mass for which a differential diagnosis was challenging. The mass persisted after administration of intravenous antibiotic therapy for 1 month. A computed tomography scan indicated an inflammatory lesion, whereas magnetic resonance imaging suggested a diagnosis of a tumor. Despite these indications, a right renal abscess was suspected during robot-assisted laparoscopic surgery, and nephron-sparing surgery was performed, which allowed confirmation of the final pathological result by biopsy specimen. Postoperatively, the mass gradually decreased in size after antibiotic therapy for a further month. This case, in which a renal abscess mimicked a tumor and the patient almost underwent a nephrectomy, highlights the need for caution in establishing therapeutic schedules for patients with inaccurate diagnoses. The management strategies for such patients must be reviewed and improved.

In our floral industry trend is a very important factor. As a university we are very aware of this phenomenon. Design and trends are always moving and changing and we have built this into the teaching program that we offer. We take the students for excursions to visit trend stock marketing, closely follow trend watchers, keeping an eye on the trends of fashion and living. But we have also developed assignments where students do research for innovation at their internship. So we know how to deal with these changes and to prepare students for this phenomenon. In this paper I would like to put forward how and if we can develop mental development and mental flexibility by using creative reflection and thereby preparing students for a fast world. At this moment we already do train the students to look differently to plant material and technique, in another way of designing and how to make choices. It’s important to distinguish oneself as a florist at this high level (level 5 and level 6) in the floral industry. It’s important to move with trends and have an innovative authentic vision of floral design. [ABSTRACT FROM AUTHOR]

Background: There remains a demand for a practical method of identifying lipid-poor adrenal lesions. Purpose: To explore the predictive value of computed tomography (CT) features combined with demographic characteristics for lipid-poor adrenal adenomas and nonadenomas. Materials and Methods: We retrospectively recruited patients with lipid-poor adrenal lesions between January 2015 and August 2021 from two independent institutions as follows: Institution 1 for the training set and the internal validation set and Institution 2 for the external validation set. Two radiologists reviewed CT images for the three sets. We performed a least absolute shrinkage and selection operator (LASSO) algorithm to select variables; subsequently, multivariate analysis was used to develop a generalized linear model. The probability threshold of the model was set to 0.5 in the external validation set. We calculated the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) for the model and radiologists. The model was validated and tested in the internal validation and external validation sets; moreover, the accuracy between the model and both radiologists were compared using the McNemar test in the external validation set. Results: In total, 253 patients (median age, 55 years [interquartile range, 47-64 years]; 135 men) with 121 lipid-poor adrenal adenomas and 132 nonadenomas were included in Institution 1, whereas another 55 patients were included in Institution 2. The multivariable analysis showed that age, male, lesion size, necrosis, unenhanced attenuation, and portal venous phase attenuation were independently associated with adrenal adenomas. The clinical-image model showed AUCs of 0.96 (95% confidence interval [CI]: 0.91, 0.98), 0.93 (95% CI: 0.84, 0.97), and 0.86 (95% CI: 0.74, 0.94) in the training set, internal validation set, and external validation set, respectively. In the external validation set, the model showed a significantly and non-significantly higher accuracy than reader 1 (84% vs. 65%, P = 0.031) and reader 2 (84% vs. 69%, P = 0.057), respectively. Conclusions: Our clinical-image model displayed good utility in differentiating lipid-poor adrenal adenomas. Further, it showed better diagnostic ability than experienced radiologists in the external validation set. [ABSTRACT FROM AUTHOR]

Distribution and sharing are social preference behaviors supported and shaped by selection pressures, which express individuals’ concern for the welfare of others. Distributive behavior results in distributive justice, which is at the core of moral justice. Sharing is a feature of the prosocial realm. The connotations of distribution and sharing are different, so the principles, research paradigms, and social functions of the two are also different. Three potential causes of confusion between the two in the current research on distribution and sharing are discussed. First, they share common factors in terms of individual cognition, situation, and social factors. Second, although they are conceptually different, prosocial sharing and distribution fairness sensitivity are mutually predictive in individual infants. Similarly, neural differences in preschoolers’ perception of distribution fairness predict their subsequent sharing generosity. Finally, similar activation regions are relevant to distribution and sharing situations that need behavioral control on a neural basis. [ABSTRACT FROM AUTHOR]

Developing rapid and user-friendly visual techniques for distinguishing and identifying heavy metal ions is a critical pursuit in food and environmental analysis. In this work, a zirconium-based metal organic framework (MOF) known as PCN-222 has been synthesized, serving as a colorimetric nanoplatform for the differentiation and detection of Cd2+ and Pb2+ in the presence of PO 4 3-. The remarkable characteristic of PCN-222 stems from its reaction with PO 4 3-, where the robust bonding between PO 4 3- and Zr nodes triggers the collapses of PCN-222, leading to the release of ligands (TCPP) with visible alteration in solution color to light red. When exposure to Cd2+ and Pb2+, shifts to green and yellow, respectively, are discernible. These changes can be ascribed to differences in the size, atomic configuration, and electronegativity of the two metal ions, which impact their interaction with TCPP. The innovative concept of a nanomaterial traffic light is introduced based on these color transitions. Accurate quantification of Cd2+ and Pb2+ can be efficiently accomplished both individually and concurrently using UV spectroscopy and a mobile phone APP, providing adaptability in method selection depending on on-site conditions. Moreover, the platform effectively tackles selectivity challenges associated with ligand-based detection methods, showcasing high sensitivity, distinct visual indications, and simplicity in instrumentation requirements. [Display omitted] • Traffic lights inspired colorimetric sensing platform for cadmium/lead ions was developed. • The mechanism for PO 4 3--induced structural changes of porphyrin metal organic framework was studied. • The concept of simulation traffic light visual nanoplatform was proposed for the first time. • The sensing platform can achieve individual and simultaneous detection of Cd2+ and Pb2+ ions. • The sensing platform shows high selectivity and sensitivity with obvious phenomena. [ABSTRACT FROM AUTHOR]

Cultural fit is widely believed to affect the success of individuals and the groups to which they belong. Yet it remains an elusive, poorly measured construct. Recent research draws on computational linguistics to measure cultural fit but overlooks asymmetries in cultural adaptation. By contrast, we develop a directed, dynamic measure of cultural fit based on linguistic alignment, which estimates the influence of one person’s word use on another’s and distinguishes between two enculturation mechanisms: internalization and selfregulation. We use this measure to trace employees’ enculturation trajectories over a large, multi-year corpus of corporate emails and find that patterns of alignment in the first six months of employment are predictive of individuals downstream outcomes, especially involuntary exit. Further predictive analyses suggest referential alignment plays an overlooked role in linguistic alignment.

Since 2013, frequent haze pollution events in China have been attracting public attention, generating a demand to identify the haze areas using satellite observations. Many studies of haze recognition algorithms are based on observations from space-borne imagers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Advanced Himawari Imager (AHI). Since the haze pixels are frequently misidentified as clouds in the official cloud detection products, these algorithms mainly focus on recovering them from clouds. There are just a few studies that provide a more precise distinction between haze and clear pixels. The Medium Resolution Imaging Spectrometer II (MERSI-II), the imager aboard the FY-3D satellite, has similar bands to those of MODIS, hence, it appears to have equivalent application potential. This study proposes a novel MERSI haze mask (MHAM) algorithm to directly categorize haze pixels in addition to cloudy and clear ones. This algorithm is based on the fact that cloudy and clear pixels exhibit opposing visible channel reflectance and infrared channel brightness temperature characteristics, and clear pixels are relative brighter, and as well as this, there is a positive difference between their apparent reflectance values, at 0.865 μm and 1.64 μm, respectively, over bright surfaces. Compared with the Aqua/MODIS and MERSI-II official cloud detection products, these two datasets treat the dense aerosol loadings as certain clouds, possible clouds and possible clear pixels, and they treat distinguished light or moderate haze as possible clouds, possible clear pixels and certainly clear pixels, while the novel algorithm is capable of demonstrating the haze region’s boundary in a manner that is more substantially consistent with the true color image. Using the PM2.5 (particle matter with a diameter that is less than 2.5 μm) data monitored by the national air quality monitoring stations as the test source, the results indicated that when the ground-based PM2.5 ≥ 35 μg/cm3 is considered to be haze days, the samples with the recognition rate that is higher than 85% accounted for 72.22% of the total samples. When PM2.5 ≥ 50 μg/cm3 is considered as haze days, 83.33% of the samples had an identification rate that was higher than 85%. A cross-comparison with similar research methods showed that the method proposed in this study had better sensitivity to bright surface clear and haze areas. This study will provide a haze mask for subsequent quantitative inversion of aerosol characteristics, and it will further exert the application benefits of MERSI-II instrument aboard on FY3D satellite.

Raman spectroscopy and statistics were used to identify different brands and types of soap accurately and quickly. The Raman spectra of 56 kinds of soaps from different manufacturers and brands were collected, and the samples were divided into five categories through Raman spectrum preprocessing and cluster analysis. In the use of SVM, KNN, Bayesian method to establish discriminant analysis, the accuracy of the test discrimination comparison, comprehensive comparison of KNN model classification effect is the best, the classification accuracy is 96.4%.This method can be used to test soap samples quickly and accurately, which provides a new method for the identification of soap samples in the process of handling cases. [ABSTRACT FROM AUTHOR]

To distinguish different adulterated vegetable oils in safflower seed oil, counterfeit vegetable oils were identified and quantified. The near-infrared hyperspectral reflectance curve data of four oil samples were obtained in the range of 387–1035 nm. The spectrum data was preprocessed by five methods and combined with four algorithm models to predict different types of oil samples. The results showed that the median filtering algorithm combined with the Gradient Boosting Decision Tree model can achieve a 100% recognition rate for four kinds of oil samples. [ABSTRACT FROM AUTHOR]

Copyright of Food & Machinery is the property of Food & Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Roa Iktissadia Review is the property of Roa Iktissadia Review and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: We are aiming to build a supervised machine learning-based classifier, in order to preoperatively distinguish focal cortical dysplasia (FCD) from glioneuronal tumors (GNTs) in patients with epilepsy.Methods: This retrospective study was comprised of 96 patients who underwent epilepsy surgery, with the final neuropathologic diagnosis of either an FCD or GNTs. Seven classical machine learning algorithms (i.e., Random Forest, SVM, Decision Tree, Logistic Regression, XGBoost, LightGBM, and CatBoost) were employed and trained by our dataset to get the classification model. Ten features [i.e., Gender, Past history, Age at seizure onset, Course of disease, Seizure type, Seizure frequency, Scalp EEG biomarkers, MRI features, Lesion location, Number of antiepileptic drug (AEDs)] were analyzed in our study.Results: We enrolled 56 patients with FCD and 40 patients with GNTs, which included 29 with gangliogliomas (GGs) and 11 with dysembryoplasic neuroepithelial tumors (DNTs). Our study demonstrated that the Random Forest-based machine learning model offered the best predictive performance on distinguishing the diagnosis of FCD from GNTs, with an F1-score of 0.9180 and AUC value of 0.9340. Furthermore, the most discriminative factor between FCD and GNTs was the feature “age at seizure onset” with the Chi-square value of 1,213.0, suggesting that patients who had a younger age at seizure onset were more likely to be diagnosed as FCD.Conclusion: The Random Forest-based machine learning classifier can accurately differentiate FCD from GNTs in patients with epilepsy before surgery. This might lead to improved clinician confidence in appropriate surgical planning and treatment outcomes.

Purpose: We are aiming to build a supervised machine learning-based classifier, in order to preoperatively distinguish focal cortical dysplasia (FCD) from glioneuronal tumors (GNTs) in patients with epilepsy. Methods: This retrospective study was comprised of 96 patients who underwent epilepsy surgery, with the final neuropathologic diagnosis of either an FCD or GNTs. Seven classical machine learning algorithms (i.e., Random Forest, SVM, Decision Tree, Logistic Regression, XGBoost, LightGBM, and CatBoost) were employed and trained by our dataset to get the classification model. Ten features [i.e., Gender, Past history, Age at seizure onset, Course of disease, Seizure type, Seizure frequency, Scalp EEG biomarkers, MRI features, Lesion location, Number of antiepileptic drug (AEDs)] were analyzed in our study. Results: We enrolled 56 patients with FCD and 40 patients with GNTs, which included 29 with gangliogliomas (GGs) and 11 with dysembryoplasic neuroepithelial tumors (DNTs). Our study demonstrated that the Random Forest-based machine learning model offered the best predictive performance on distinguishing the diagnosis of FCD from GNTs, with an F1-score of 0.9180 and AUC value of 0.9340. Furthermore, the most discriminative factor between FCD and GNTs was the feature "age at seizure onset" with the Chi-square value of 1,213.0, suggesting that patients who had a younger age at seizure onset were more likely to be diagnosed as FCD. Conclusion: The Random Forest-based machine learning classifier can accurately differentiate FCD from GNTs in patients with epilepsy before surgery. This might lead to improved clinician confidence in appropriate surgical planning and treatment outcomes. [ABSTRACT FROM AUTHOR]

Abstract. In the nature, difficulties with distinguishing of closely related species like Stone marten (Martes foina) and Pine marten (Martes martes) appear. Based on 280 photos a non-invasive method for determining the Stone marten and the Pine marten by camera trapping was performed. Sloping trees (similar to Leaning-Pole or Running-Pole) were used as a route for animals to pass with a purpose to be photographed. Honey and distal parts of bird wings were used as a lure. The camera was set to capture the climbing animal from the side. The coat coloration and scent marking patterns - proper species indicators - were clearly visible on daily photos. The night photos showed the differences in body proportion but not these in throat patch coloration. Profile photographing also allowed an individual determination based on throat patch shape and area.

Objective　To distinguish the structural analogues xanthine, theophylline and theobromine by surfaceenhanced Raman spectroscopy. Methods　Concentrated silver colloid enhancement reagent was prepared as the Raman substrate to increase the number of "hot spots" per unit area, improve the sensitivity of surface-enhanced Raman spectroscopy, enhance the signal strength of the samples and achieve the effective discrimination of structural analogues. Meanwhile, the feasibility of surfaceenhanced Raman spectroscopy in practical application was verified by determining serum samples of three mixtures. Results　The concentrated silver colloid greatly increased the Raman intensity of the three structural analogues. The spectra of each individual compound and the mixture in the serum system was obtained. The detection limit of the three substances in aqueous solution were 0.005, 0.01 and 0.005 μmol/L respectively. Conclusion　Surface-enhanced Raman spectroscopy is a potent technique for distinguishing structural analogues. It is rapid, sensitive and nondestructive to samples. Hence, it can be widely used in the fields of detection, analysis, clinical treatment and diagnosis. [ABSTRACT FROM AUTHOR]

Background: To distinguish preinvasive (adenocarcinoma in situ/atypical adenomatous hyperplasia) and minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IA) appearing as solitary subsolid nodules (SSNs) less than 3 cm based on thin-section computed tomography (TSCT) features to guide therapeutic approaches.Methods: A total of 154 lesions that were histopathologically confirmed to have pre/minimally invasive adenocarcinoma (hereafter pre/MIA) and IA presenting as part-solid nodules (PSNs) or pure ground-glass nodules (pGGNs) were retrospectively reviewed. The TSCT features, including diameter, area, CT value, shape, air bronchogram, margins, and location, were compared and assessed. Receiver operating characteristic analyses were conducted to determine the cut-off values for the qualitative variables and their diagnostic performances.Results: Of 154 nodules, 89 IA, 53 MIA, eight adenocarcinoma in situ, and four atypical adenomatous hyperplasia lesions were found. Univariate and multivariate logistic regression of the pre/MIA and IA lesions were compared and analyzed among PSNs and pGGNs. Among pGGNs, a significant difference was found in the area (p = 0.004, odds ratio [OR] = 0.124, 95% confidence interval [CI] = 0.300-0.515) between the pre/MIA and IA groups. In PSNs, significant differences were found in the diameter (p = 0.001, OR = 0.171, 95% CI = 0.063-0.467) and CT value (p = 0.001, OR = 0.996, 95% CI = 0.993-0.998) between the pre/MIA and IA groups. According to the corresponding receiver operating characteristic curves, the optimal cut-off tumor area in pGGNs to differentiate pre/MIA from IA was 0.595 cm2. A higher CT value of the lesion (≥ -298.500 HU) and a larger diameter (≥1.450 cm) in PSNs were significantly associated with IA.Conclusion: Imaging features from TSCT contribute to distinguishing pre/MIA from IA in solitary subsolid nodules and may contribute to guide the clinical management of these lesions. [ABSTRACT FROM AUTHOR]

From the perspective of philosophical classification of information forms, calligraphy appreciation can be divided into the stages self-contained information of objective and indirect existence, self-contained information of a subject’s intuitive recognition, and regenerative information of subjective initiative creation. In the self-information stage of the indirect existence of an objective view, the calligraphy works themselves have not been recognized and grasped during appreciation and the calligraphy works are only the objective form externalizing the subjective spirit of the creative subject. In the self-information stage of a subject’s intuitive recognition, the spatiotemporal information, the semantic information, technical information, and aesthetic information condensed in the works are directly recognized by the appreciator. In the regenerated information stage of subjective initiative creation, the appreciator subjectively reprocesses and recreates on the basis of the overall grasp of space–time information, semantic information, technical information, and aesthetic information. It includes two forms, the analogy of “skeuomorphism” and the extension of “potential”.

There is a requirement to develop an novel vaccine to distinguish between vaccinated and infected animals after vaccination. Two mutants of Brucella melitensis M5-90Δbp261 and M5-90Δbp262, knockout of the fragments amino acids 55-152 and 22-185 respectively, were primarily generated. Then the two mutants were inoculated 7 weeks old BALB/c mice with 3.0 × 106 CFU/0.2 mL parent or mutant strains, Serum samples were evaluated by Rose Bengal plate tests, serum agglutination tests, colloidal gold immunochromatographic strips (ICS) coated with VirB5 or BP26 and indirect enzyme-linked immunosorbent assays. Spleen tissue samples were used to determine B. melitensis abundance at 10, 20, 30, and 40 days post-immunization (dpi). The serological results showed that the parent and mutant strains elicited a weak immunological reaction at 10–30 dpi. Fewer colony forming units (CFUs) were recovered from spleens in mutant strains group than the parent strain group during 20-40 post-immunization (dpi). The ICS were able to distinguish between the sera from mice immunized with either a parent or mutant strain of B. melitensis. These results indicate that the two mutants strain of B. melitensis have potentially perspectives in distinguishing infected animals from vaccination combined with ICS in this study.

Natural image matting is an important image processing task. How to leverage the advantages of both sampling matting and propagation matting is a challenge issue. In this paper, we propose a novel sampling-propagation matting method. First, in an overall framework, we propose a three-stage method for sampling-propagation matting, in which the sampling matting stage and propagation matting stage are bridged by a new stage (stage 2). Second, in the sampling matting stage (stage 1), a new gradient sampling matting is presented to cover more diversified samples, and a new equation is proposed to calculate the impact of sample-pair overlap. Third, in bridge stage (stage 2), we propose a judgment criterion to distinguish each pixel of matte after stage 1. For pixels that fail to meet the criterion, we propose an automatic labeling method. Fourth, in the propagation stage (stage 3), we discriminatingly process non-labeled pixels and labeled pixels with separate weight coefficients. The non-labeled pixels are smoothed by propagation, and the labeled pixels are solved by propagation matting. Finally, the proposed method is compared with other methods on public available benchmark. The results show that our method outperforms many other methods and achieves a good ranking.

Abstract For the study of nonlinear dynamic characteristics of Biophoton Emission signal of wheat kernels, the permutation entropy is introduced to distinguish normal kernels and infected ones. The utility of permutation entropy for wheat signal with different degree of ultra weak luminescence is investigated, and the effect of embedded dimension and time delay on permutation entropy is studied. Experimental results on ultra weak luminescence signal of wheat analysis confirm that the permutation entropy provides an effective measure for testing the difference between normal wheat and infected wheat. [ABSTRACT FROM AUTHOR]

Objective: Our study aimed to assess the predictive value of the preoperative neutrophil-to-lymphocyte ratio(NLR) in distinguishing sarcomatoid renal cell carcinoma (SRCC) from clear cell renal cell carcinoma(CCRCC) and to developing a nomogram based on the preoperative NLR and other factors to distinguish SRCC from CCRCC., Materials and Methods: The database involved 280 patients, including 46 SRCC and 234 CCRCC. logistic analysis was conducted to select the variables associated with identifying SRCC preoperatively, and subgroup analysis was used to further validate the ability of NLR with preoperative identification of SRCC.In addition, The data were randomly separated into a training cohort(n=195) and a validation cohort(n=85). And an NLR-based nomogram was plotted based on the logistic analysis results. The nomogram was evaluated according to its discrimination, consistency, and clinical benefits., Results: Multivariate analysis indicated that NLR, flank pain, tumor size, and total cholesterol(TC) were independent risk factors for identifying SRCC. The results of subgroup analysis showed that higher NLR was associated with a higher probability of SRCC in most subgroups. The area under the curve(AUC) of the training and validation cohorts were 0.801 and 0.738, respectively. The results of the calibration curve show high consistency between predicted and actual results. Decision Curve Analysis(DCA) showed clinical intervention based on the model was beneficial over most of the threshold risk range., Conclusion: NLR is a potential indicator for preoperative differentiation of SRCC and CCRCC, and the predictive model constructed based on NLR has a good predictive ability. The new model could provide suggestions for the early identification of SRCC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wu, Qi, Qin, Zhao, Zheng, Du and Yu.)

The identification of Trichogramma (Hymenoptera: Trichogrammatidae) species is problematic due to their small size and lack of distinct morphological characters. In this study, we combined morphological characters of the male genitalia and molecular methods using the mitochondrial cytochrome oxidase I (COI) gene as a molecular marker to identify eight species from 16 geographic populations: T. evanescens Westwood, T. cacoeciae Marchal, T. ostriniae Pang et Chen, T. chilonis Ishii, T. japonicum Ashmead, T. brassicae Bezdenko, T. bilingensis He et Pang, and T. dendrolimi Matsumura. We developed a polymerase chain reaction–restriction fragment length polymorphism (PCR–RFLP) method targeting the mitochondrial COI to distinguish the eight Trichogramma species using three restriction enzymes. We further analyzed 109 COI fragments from 18 Trichogramma species and found that the PCR–RFLP method could distinguish both intra- and inter-specific genetic variation among most of the species using four additional restriction enzymes. [ABSTRACT FROM AUTHOR]

Terahertz spectroscopy, combined with a metamaterial resonator, was used to distinguish varieties of herbs that look alike to the naked eye. The metamaterial resonator, based on an ohm-ring, was experimentally demonstrated to exhibit two significant resonant peaks in the range from 0.2 to 1.4 THz. The resonant peaks were shifted to lower frequency with changes of the surface permittivity, and this feature was applied to identify varieties of herbs. In the experiment, different borneols were effectively identified according to the different degree of redshift. This work demonstrates that metamaterial resonator is a sensitive tool for terahertz spectral sensing, and shows a new detection method for different herbs. [ABSTRACT FROM AUTHOR]

An approach to distinguish eight kinds of different human cells by Raman spectroscopy was proposed and demonstrated in this paper. Original spectra of suspension cells in the frequency range of 623~1783 cm-1 were acquired and pre-processed by baseline calibration, and principal component analysis (PCA) was employed to extract the useful spectral information. To develop a robust discrimination model, a linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were attempted comparatively in the work. The results showed that the QDA model is better than the LDA model. The optimal QDA model was generated with 12 principal components. The classification rates are 100% in the calibration and prediction set, respectively. From the experimental results, it is concluded that Raman spectroscopy combined with appropriate discriminant analysis methods has significant potential in human cell detection. [ABSTRACT FROM AUTHOR]

There is a requirement to develop an novel vaccine to distinguish between vaccinated and infected animals after vaccination. Two mutants of Brucella melitensis M5-90Δbp261 and M5-90Δbp262, knockout of the fragments amino acids 55-152 and 22-185 respectively, were primarily generated. Then the two mutants were inoculated 7 weeks old BALB/c mice with 3.0 × 106 CFU/0.2 mL parent or mutant strains, Serum samples were evaluated by Rose Bengal plate tests, serum agglutination tests, colloidal gold immunochromatographic strips (ICS) coated with VirB5 or BP26 and indirect enzyme-linked immunosorbent assays. Spleen tissue samples were used to determine B. melitensis abundance at 10, 20, 30, and 40 days post-immunization (dpi). The serological results showed that the parent and mutant strains elicited a weak immunological reaction at 10-30 dpi. Fewer colony forming units (CFUs) were recovered from spleens in mutant strains group than the parent strain group during 20-40 post-immunization (dpi). The ICS were able to distinguish between the sera from mice immunized with either a parent or mutant strain of B. melitensis. These results indicate that the two mutants strain of B. melitensis have potentially perspectives in distinguishing infected animals from vaccination combined with ICS in this study. [ABSTRACT FROM AUTHOR]

A methodology is proposed to identify transgenic sugar beet from non-transgenic ones by using terahertz spectroscopy combined with chemometric techniques in this paper. Principal component analysis (PCA) is applied to reduce the dimension and extract the feature spectrum of original spectral information. instead of original spectral information, the characteristic spectrum is selected to fed into the model of weighted linear discriminate analysis (WLDA)which an improved linear discriminate analysis method. The experimental results indicates that the terahertz spectroscopy coupled to PCA-WLDA can provide a rapid, nondestructive and reliable method to distinguish transgenic and non-transgenic sugar beets. [ABSTRACT FROM AUTHOR]

There are some irrelevant or low correlation features in physical education teaching pattern recognition, resulting in low recognition accuracy. A three-dimensional integrated process physical education teaching pattern recognition method based on big data is designed. The physical education teaching data are cleaned, integrated, normalized and discretized, and the data attribute features are extracted using big data technology. The results of an attribute in all lifting decision trees are weighted and averaged to obtain the characteristic importance score of the attribute. The teaching pattern classification algorithm is designed according to the attribute characteristics, and the trace of the sample divergence matrix is used to increase the speed of solving the parameters and weights. Through the preliminary and secondary training of neural network, a physical education pattern recognition model is established to ensure the accuracy of small sample feature set recognition. It shows that the recognition accuracy of this method is significantly higher than that of the recognition methods based on the random forest algorithm and convolution neural network, and it has a good performance in physical education teaching pattern classification and recognition. [ABSTRACT FROM AUTHOR]

A novel adaptive dynamic particle swarm optimization (ADPSO) combined with support vector machine (SVM) is proposed for Transgenic Organisms distinguish based on terahertz spectroscopy (THz). The ADPSO-SVM algorithm includes perception and an updated mechanism. In each screening step, the algorithm can perception the change of the external environment and update the particle swarm to obtain the global optimal solution. Some transgenic cotton seeds data set mimicking the terahertz spectra of tissue samples are applied to evaluate the performance of the ADPSO-SVM. The experimental results show that, the recognition rate of ADPSO-SVM reaches to 96.970%. Therefore, the ADPSO-SVM can provide an accurate, fast, simple, and nondestructive detection method for transgenic cotton combined with terahertz spectroscopy. [ABSTRACT FROM AUTHOR]

The Hindko consonantal system consists of twelve stops, eight fricatives, three nasals, three liquids and one glide. Acoustically, these segments are distinguishable on the bases of cues. This paper studies affricates, nasals, liquids and the lone glide in Hindko to distinguish these on the bases of acoustic properties. The acoustic analysis divulges that all the groups are clearly distinguishable. The affricates demonstrate the difference in terms of closure, friction and total duration in a hierarchical order. The nasals reveal that the formants are fainter than the preceding and following vowels and these have lower F2 and F3 even lower thanEnglish nasals. Among liquids, lateral /l/ appears acoustically like nasals, trill /r/ is identified by broken formants while flop /t/ by downward transition of F3. The lone palatal glide /j/ by a small gap between F2 and F3 as the former moves to the latter. [ABSTRACT FROM AUTHOR]